Senior Consultant, Forward Deployed Engineer, TC, FS

As a Forward Deployed Engineer, you will contribute to the design and delivery of AI-enabled systems that operate reliably in enterprise environments. Working within a delivery squad, you will take ownership of defined engineering components and support the development of production-ready AI capabilities. You will work at the intersection of software engineering, product thinking and client delivery, helping translate complex problems into working solutions deployed in real-world environments. In this Senior Consultant role, you will operate as a hands-on engineer within a squad, designing and implementing AI capabilities such as agents, RAG pipelines and supporting services while ensuring solutions meet enterprise standards for reliability, security and governance. What you'll do Client-facing engineering & delivery

• Collaborate with client stakeholders and internal teams to understand requirements and translate them into engineering tasks and deliverables.
• Contribute to workshops, demos and solution reviews while communicating technical considerations clearly to mixed audiences.
• Support the adoption of delivered solutions through testing, iteration and feedback cycles.
Solution design & implementation
• Design and implement AI-enabled components such as RAG pipelines, agents and supporting microservices.
• Integrate solutions with enterprise data sources, APIs and existing platforms while maintaining reliability and security standards.
• Apply responsible-AI guardrails, monitoring and operational practices to ensure safe and maintainable deployments.
Product mindset & continuous improvement
• Contribute to defining MVP scope, engineering tasks and delivery milestones within the squad.
• Implement evaluation approaches, monitoring and dashboards to understand system performance and user impact.
• Capture reusable engineering patterns and contribute to shared accelerators used across projects., Software engineering: Python and/or TypeScript, async patterns, testing/CI, API design and microservice fundamentals.
• RAG engineering: retrieval strategies, chunking/indexing, hybrid search, grounding and hallucination mitigation.
• Applied AI/ML: hands-on with LLMs, embeddings and vector databases (FAISS/Milvus/Pinecone).
• LLMOps & evaluation: prompt pipelines, automated/offline evaluation, safety/guardrails, telemetry, versioning and CI/CD for ML.
• Cloud engineering: Azure (preferred) and/or AWS/GCP; Kubernetes/Docker; serverless (Azure Functions/Lambda); secure networking/IAM.
• Data engineering: Spark/Databricks, ETL/ELT pipelines; integration with client data sources and enterprise systems; runbooks and operational handover.
• Product delivery: workshop facilitation, PRDs, user stories and acceptance criteria; backlog ownership; roadmap and release planning.
• Measurement: define value/adoption metrics; implement simple dashboards or analysis to evidence impact and inform iteration.
• Responsible AI & compliance: privacy-by-design, model-risk, auditability; awareness of UK expectations (FCA, PRA, GDPR).
• Consulting behaviours: client-ready communication, time management and collaboration in diverse, multi-disciplinary teams.
Nice to have
• Document & graph stores (e.g., Neo4j) and streaming patterns (Event Hub/Kafka).
• Azure/AWS Solutions Architect exposure; optional governance/model-risk/responsible-AI credentials.
Technical Certifications (preferred)
• Microsoft Azure AI Engineer Associate (AI-102) or Azure Data Scientist Associate.
• AWS Machine Learning Specialty or Google Professional ML Engineer.
• Databricks (Data Engineer/ML Engineer) and Kubernetes (CKA/CKAD).
• Azure/AWS Solutions Architect; optional model-risk/responsible-AI governance credentials., Hybrid working and periodic travel to client sites across the UK (and occasionally internationally), discussed based on projects and location.
  
  EY is investing in engineering-led AI at scale. EY has been selected for the inaugural Frontier Firm AI Initiative - a collaboration between Microsoft and Harvard's Digital Data Design Institute - recognising EY's leadership in shaping enterprise-grade human-AI operating models.
In parallel, EY is expanding its collaboration with OpenAI and Microsoft, bringing advanced AI capabilities to clients through Microsoft's secure Azure OpenAI Service. Joining this team means being part of a growing engineering capability focused on building production-grade AI systems for major organisations across sectors. The opportunity Organisations are moving rapidly from AI experimentation to operational adoption. However, many struggle to translate ideas into secure, scalable and reliable production solutions., EY is committed to building an inclusive culture where everyone can thrive. If you require adjustments or support during the recruitment process, we encourage you to let us know. EY | Building a better working world EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets. Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow. EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.


• You're comfortable being hands-on and shipping in short cycles while staying pragmatic about trade-offs.
• You can explain trade-offs simply and earn trust with clients and teammates; you operate well in ambiguity and prioritise quickly.
• You care about quality, operability and safe iteration (runbooks/observability/rollback).
What we offer High-impact work with leading organisations across sectors, within a collaborative engineering-led AI capability. You will benefit from:
• Continuous development through the FDE Academy, strengthening the technical capabilities required to build and operate AI systems in production environments.
• Opportunities to participate in hackathons, engineering showcases and innovation challenges.
• Learning and certification support across cloud, AI and engineering platforms.
• Competitive compensation and benefits.
• Flexible hybrid working arrangements depending on client needs.